spotlight
EnvisEdge
| spotlight | EnvisEdge | |
|---|---|---|
| - | 2 | |
| 2,934 | 135 | |
| - | - | |
| 0.0 | 3.5 | |
| over 1 year ago | 10 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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spotlight
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Tracking mentions began in Dec 2020.
EnvisEdge
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A new way to build decentralised recommendation engines for the creator economy
Hear me out on what I think a truly decentralised content curation.
Twitter, FB (Meta), Youtube everyone harvests user data and train their recommendation engines which are then monetised by them (often unfairly).
In the future, the data stays on the users' devices and anyone can train their models by asking the user for the consent. THe data never leaves the device and ML models get trained on user device itself. The users get to choose from a host of recommendation choices and can ask for payment in return for using their data. So no one party can build a monopoly over the platform.
Check out a cool project I have been working on to solve this https://github.com/NimbleEdge/RecoEdge
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Ask HN: What cutting-edge technology do you use?
Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between each other and give me the best performance out there. I have my own local cloud formed by my laptop, smartphone and ipad.
I built out the library for these myself, checkout https://github.com/NimbleEdge/RecoEdge
What are some alternatives?
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TensorRec - A TensorFlow recommendation algorithm and framework in Python.
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libffm - A Library for Field-aware Factorization Machines
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